With modem technology it has been possible to collect extensive records, which describe attributes of subjects of the records. In particular, companies can develop records that describe many attributes of their customers. It would be desirable if these records could be used to predict which customers, i.e., subjects of the records, are relatively more likely to have a particular characteristic which is not explicitly described by the attributes in the records. That is, it would be desirable if the records could be rank ordered in a manner that correlates with the relative likelihood that the corresponding customers would, for example, buy a particular product. It would be particularly desirable if the records could be rank ordered in a manner correlating with the relative likelihood that customers would become bad debt risks. By “bad debt risk”, or sometimes “toll risk” in the context of long distance services, herein is meant a customer who is sufficiently delinquent in payment to create a substantial risk of non-payment.
This need is particularly acute for long distance telephone carriers where at any time totals of hundreds of millions of dollars are owed by millions of customers for long distance telephone service. The need for an early identification of toll risks is even greater for carriers, such as the assignee of the present invention, who have a “no-denial” policy; that is carriers who do not use any criteria to deny service to customers. Previously the above mentioned assignee has used two “toll risk” strategies to deal with this bad debt problem: a High Toll System (HTS), and a Collection Strategy System (CSS). The High Toll System generates alarms based on dollar amounts and call detail thresholds. The dollar amount and call detail from the High Toll alarm are then compared to the customer's normal billing history. Computer algorithms and/or human analysis are then used to determine if the customer's service should be deactivated, sometimes hereinafter referred to “blocking the account”. The other system, the Collection Strategy System, is a time based collection management system triggered from the last paid invoice. CSS used a commercial credit score to predict the likelihood of an applicant or established customer becoming a serious credit risk to determine which collection strategy to use for each of the applicants or customers. Currently CSS uses various collection strategies and timelines based on the calculated risk of bad debt before a warning is sent or other action is taken.
While the above described systems have proven to be somewhat effective it would clearly be desirable to reduce the losses attributed to bad debt. Further, it has been found that as much as two thirds of all write-offs come from one third of new customers. Consequently there is a need for a system that would allow prompt analysis of the behavior of new customers and allow early intervention to minimize delinquencies.